linreg-core 0.8.1

Lightweight regression library (OLS, Ridge, Lasso, Elastic Net, WLS, LOESS, Polynomial) with 14 diagnostic tests, cross validation, and prediction intervals. Pure Rust - no external math dependencies. WASM, Python, FFI, and Excel XLL bindings.
Documentation 
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{
  "test_name": "Cook's Distance (Python - statsmodels)",
  "dataset": "lh",
  "formula": "time ~ value",
  "distances": [
    0.03371420268447678,
    0.0309059576442125,
    0.02821981021439454,
    0.024631069777219433,
    0.02316033449476557,
    0.030267169046458934,
    0.01752381820531546,
    0.015482441039897502,
    0.01688513112905778,
    0.011857185027002856,
    0.009993207075491565,
    0.007488626120121244,
    0.006697097765326741,
    0.004170579687907896,
    0.05590057181169878,
    0.04924204524647665,
    0.008117786149145958,
    0.00160878296372088,
    0.0010089774019236188,
    8.21940392567358e-06,
    6.275549049899146e-05,
    0.0009462223290252005,
    0.0013146337675140982,
    0.004424046912521851,
    0.00011462427614046132,
    0.002293555335261018,
    0.0033281290707302806,
    6.275549049899123e-05,
    8.219403925673549e-06,
    0.000705246641024063,
    0.001262378061232194,
    0.004402320537215955,
    0.003226251485839309,
    0.005509654671388009,
    0.03448057179993789,
    0.052661963820737935,
    0.103556273047469,
    0.1488643333407956,
    0.023382421246463614,
    0.01587506189407796,
    0.01911038841892617,
    0.024800418966010006,
    0.0274040038077675,
    0.022081263022310563,
    0.04274721474594773,
    0.05238118546485166,
    0.04309408731225119,
    0.04320324188431898
  ],
  "p": 2,
  "mse": 177.96653998175736,
  "threshold_4_over_n": 0.08333333333333333,
  "threshold_4_over_df": 0.08695652173913043,
  "threshold_1": 1.0,
  "influential_4_over_n": [
    37,
    38
  ],
  "influential_4_over_df": [
    37,
    38
  ],
  "influential_1": [],
  "max_distance": 0.1488643333407956,
  "max_index": 38,
  "description": "Measures influence of each observation on regression coefficients. Uses statsmodels.stats.outliers_influence.OLSInfluence.cooks_distance."
}